The present disclosure relates to rotary winged aircraft. More specifically, the present disclosure relates to health assessment of systems and components of a rotary wing aircraft.
On-board systems for health management of aircraft have to-date focused on component-specific anomaly detection and diagnoses in isolation from other component diagnostic considerations. Prior technologies include mechanical diagnostics, bearing diagnostics, and rotor track and balance algorithms that detect diagnostic features and calculate condition indicators (CIs), which are designed to indicate when a mechanical part is failed or failing. Further, algorithms are used to detect statistical anomalies in data. Fault isolation reasoners are utilized to manage and reduce built-in-test and fault codes available from avionic components into likely sources of component errors. Finally, ground-based software is used to process and display data taken on aircraft, with ground-based software architectures to integrate and display all of the above information using various data models and schemas. However, higher-level (e.g., subsystem and system) interactions and decision-making has been only possible with a human in the loop interpretation of various data sources. The value extracted from today's health monitoring systems is often limited by the experience and understanding maintenance personnel possess regarding health management system outputs and component/sub-system/system level interdependencies. To maximize the value of health management systems in achieving reliable and cost/time efficient condition-based maintenance objectives, automated processes are required that can address these interdependencies and provide specific actionable maintenance recommendations to various end-users of health management system output.